In this paper, we show how support vector machine (SVM) can be

employed as a powerful tool for $k$-nearest neighbor (kNN)

classifier. A novel multi-class dimensionality reduction approach,

Discriminant Analysis via Support Vectors (SVDA), is introduced by

using the SVM. The kernel mapping idea is used to derive the

non-linear version, Kernel Discriminant via Support Vectors (SVKD).

In SVDA, only support vectors are involved to obtain the

transformation matrix. Thus, the computational complexity can be

greatly reduced for kernel based feature extraction. Experiments

carried out on several standard databases show a clear improvement

on LDA-based recognition